Difference between revisions of "Multiobjective optimization and evolutionary algorithms"
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When applying EAs to MO, multiple pareto optimal solutions are in the quest. | When applying EAs to MO, multiple pareto optimal solutions are in the quest. | ||
One of the possible EAs for exploring the MO search space is the Differential Evolution (e.g. [[jDE]]). The most known Genetic Algorithm (GA) for the MO is the Non-dominated Sorting Genetic Algorithm - II ([http://scholar.google.com/scholar?hl=en&lr=&cites=12712700205993828129 NSGA-II]) by Deb. | One of the possible EAs for exploring the MO search space is the Differential Evolution (e.g. [[jDE]]). The most known Genetic Algorithm (GA) for the MO is the Non-dominated Sorting Genetic Algorithm - II ([http://scholar.google.com/scholar?hl=en&lr=&cites=12712700205993828129 NSGA-II]) by Deb. | ||
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| + | == Journals == | ||
| + | [http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=3749 Evolutionary Computation (The MIT Press)] | ||
| + | [http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235 IEEE Transactions on Evolutionary Computation] | ||
== External links == | == External links == | ||
Revision as of 12:17, 12 March 2007
Contents
What is Multiobjective optimization
Multiobjective optimization (MO), also known as multicritera optimization is
an optimization of a vector function 
, where 
is a vector of parameters to the vector function 
, usually being a minimisation of components of with respect to Pareto efficiency.
Pareto efficiency, also known as Pareto optimality, is a best choice of criteria, such that no criterion must be worsened in order to better some another criterion. is Pareto optimal, iff does not exist 
.
What are Evolutionary Algorithms
Evolutionary Algorithms (EAs) are problem solving principles from nature applied to the metaheuristic search. Using mutation, crossover, and selection operators, stochastic search is developed to evolve better individuals in quest for (a) globally best individual(s).
When applying EAs to MO, multiple pareto optimal solutions are in the quest. One of the possible EAs for exploring the MO search space is the Differential Evolution (e.g. jDE). The most known Genetic Algorithm (GA) for the MO is the Non-dominated Sorting Genetic Algorithm - II (NSGA-II) by Deb.
Journals
Evolutionary Computation (The MIT Press) IEEE Transactions on Evolutionary Computation